André Carlos Ponce de Leon Ferreira de Carvalho in research grants and scholarships supported by FAPESP.
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This project will create an open innovation ecosystem based on artificial intelligence for smart cities able to solve societal challenges. The ecosystem should also be an area for startups and SMEs to develop and have access to smart city solutions building on the same open source and open standards and interoperability framework recommended by EU and OASC making it attractive for both Br…
This project will explore avenues for personalised healthcare planning and modelling. We will combine artificial intelligence and mathematical modelling to produce personalised diagnosis prediction, and decision support for the design of personalised treatment and diagnostic pathways. The proposed solution will ensure data privacy, fairness and model interpretability, to allow validation …
The Applied Research Center for in Artificial Intelligence, IARA, is formed by a multidisciplinary team, with national experts of internationally recognized for their competence in artificial intelligence, internet of things telecommunications and smart cities. The center, hosted in São Carlos, in the state of São Paulo, will work in network with researchers from all Brazilian regions, or…
(Only some records are available in English at this moment)
This project defines the Application Plan of the Portion of the Technical Reserve for Institucional Infrastructure Costs for Research - ICMC. The project includes resources for the maintenance of the cloud infrastructure and its datacenter, known as Cloud-ICMC. Cloud-ICMC has been jointly, since 2013, the general computational demands of all 33 ICMC research groups and this project will c…
This project is intended to oppiy the portion of the Technical Reserve for maintenance of the ICMC computing infrastructure. The project includes resources for the maintenance of the lnstitute's own cloud infrastructure, called Cloud-ICMC, and for improving the coverage of the wireless access network, mainly to attend advanced communication technologies. Since 2013, Cloud-ICMC has shared …
Without being aware, we are using technologies based on Machine learning (ML) algorithms in a growing number of our daily activities. The use of ML has made many risk and tiring activities safer, more reliable and more accurate. Despite these contributions, new demands require the development of new ML algorithms, or use of these algorithms in new and innovative ways. Two important curren…
(Only some records are available in English at this moment)
The use of Artificial Intelligence (AI), such as Convolutional Neural Networks (CNNs), has demonstrated efficiency and high assertiveness for medical applications. Despite this success, there are significant challenges to be overcome, related to the availability of "real-world data" and CNN architectures, which face challenges in terms of reliability and explainability, given that the use…
Understanding the language of life, comprised of DNA and its translation into amino acids, presents a formidable challenge for traditional methodologies due to its inherent complexity. Consequently, there has been a notable shift towards leveraging the capabilities of alignment-free techniques, particularly machine learning (ML) approaches. Among these, deep learning, specifically large l…
Autism diagnosis is made only from clinical analysis, which is often in-accessible. The use of AI could help doctors in improving the diagnosiswith biological data, such as the Functional Magnetic Resonance Imaging(fMRI). However, such complex data usually relies on the use of similarlycomplex black-box models, lacking interpretability and therefore being lessreliable. Therefore, it is im…
(Only some records are available in English at this moment)
Science has been seeking tools to assist in the early diagnosis of diseases, especially developmental disorders in children. The study and diagnosis of psychiatric disorders benefit from tools such as functional magnetic resonance imaging (fMRI), which enables the capture of brain activation through the BOLD contrast. However, these data are complex, and machine learning tools, particular…
The objective of this work is to investigate two classes of reinforcement learning algorithms for the development of High-Frequency Trading strategies: Deep Reinforcement Learning algorithms and Reinforcement Learning algorithms through Evolutionary Optimizers. Initially, we propose the development of a realistic simulator of the Brazilian stock market, with the purpose of allowing the tr…
As high-throughput sequencing technologies have emerged in recent decades, the rate of biological data collection has been amplified, at the same time as this has served as a bottleneck for further processing and analysis, given the sheer volume of this data. Considering the voluminous data coming from these technologies, the creation of fields of studies encompassing the so-called Omics …
(Only some records are available in English at this moment)
Science has been actively seeking tools to aid in the early diagnosis of diseases, including psychiatric disorders. Functional magnetic resonance imaging (fMRI) has emerged as a valuable tool for studying and diagnosing these disorders, allowing for the observation of brain activation through the BOLD signal. However, fMRI data are inherently complex, and leveraging machine learning techn…
Different microbial communities inhabit the different regions of the human body, whether mouth, gut, vagina, or others. They are collectively called the Human Microbiome (HM). These communities participate in essential processes in human health. Thus, a better understanding of the human core microbiome may help early diagnosis of disorders and contribute to potentially more efficient trea…
Forecasting is one of the main tasks of time series analysis. Selecting forecasting models is a crucial decision-making step in many real-world applications. To obtain forecasts that best explain the variables of interest in the future, it is necessary to select the model that best fits a given time series. However, the selection task can be quite computationally expensive due to many ava…
Nearest Neighbor Search (NNS) is one of the pivotal research areas in computer science. Machine learning leverages efficient NNS algorithms to speed up distance-based algorithms. However, most online nearest neighbor-based learning algorithms still rely on brute-force search strategies to this day. This research project is primarily concerned with the sliding window-based online k-Nearest…
(Only some records are available in English at this moment)
| 5 / 5 | Ongoing research grants |
| 27 / 19 | Completed research grants |
| 11 / 11 | Ongoing scholarships in Brazil |
| 73 / 47 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 14 / 14 | Completed scholarships abroad |
| 131 / 97 | All research grants and scholarships |
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